PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.06.284885v1?rss=1

Authors: Guo, S., Zhang, H., Chu, Y., Jiang, q., Ma, Y.

Abstract:

To identify the microbial markers from the complex human gut microbiome for delineating the disease-related microbial alteration is of great interest. Here, we develop a framework combining neural network (NN) and random forest (RF), resulting in 40 marker species and 90 marker genes identified from the metagenomic dataset D1 (185 healthy and 183 type 2 diabetes (T2D) samples), respectively. Using these markers, the NN model obtains higher accuracy in classifying the T2D-related samples than machine learning-based approaches. The NN-based regression analysis determines the fasting blood glucose (FBG) is the most significant association factor (P<<0.05) in the T2D-related alteration of the gut microbiome. Twenty-four marker species that vary little across the case and control samples and are often neglected by the statistic-based methods greatly shift in different stages of the T2D development, implying that the cumulative effect of the markers rather than individuals likely drives the alteration of the gut microbiome.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.05.284109v1?rss=1

Authors: Martin, B. T., Chafin, T. K., Douglas, M. R., Douglas, M. E.

Abstract:

Patterns of multi-locus differentiation (i.e., genomic clines) often extend broadly across hybrid zones and their quantification can help diagnose how species boundaries are shaped by adaptive processes, both intrinsic and extrinsic. In this sense, the transitioning of loci across admixed individuals can be contrasted as a function of the genome-wide trend, in turn allowing an expansion of clinal theory across a much wider array of biodiversity. However, computational tools that serve to interpret and consequently visualize 'genomic clines' are limited. Here, we introduce the CLINEPLOTR R-package for visualizing genomic clines and detecting outlier loci using output generated by two popular software packages, BGC and INTROGRESS. CLINEPLOTR bundles both input generation (i.e, filtering datasets and creating specialized file formats) and output processing (e.g., MCMC thinning and burn-in) with functions that directly facilitate interpretation and hypothesis testing. Tools are also provided for post-hoc analyses that interface with external packages such as ENMEVAL and RIDEOGRAM. Our package increases the reproducibility and accessibility of genomic cline methods, thus allowing an expanded user base and promoting these methods as mechanisms to address diverse evolutionary questions in both model and non-model organisms.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.287201v1?rss=1

Authors: Iweriebor, B. C., Egbule, O. S., Danso, S. O., Akujuru, E., Ibubeleye, V. T., Oweredaba, C. I., Ogharanduku, T., Manu, A., Longjohn, M. N.

Abstract:

SARS-CoV-2 is a betacoronavirus, the etiologic agent of the novel Coronavirus disease 2019 (COVID-19). In December 2019, an outbreak of COVID-19 began in Wuhan province of the Hubei district in China and rapidly spread across the globe. On March 11th, 2020, the World Health Organization officially designated COVID-19 as a pandemic. Across the continents and specifically in Africa, all index cases were travel related. Thus, it is crucial to compare COVID-19 genome sequences from the African continent with sequences from COVID-19 hotspots (including China, Brazil, Italy, United State of America and the United Kingdom). To identify if there are distinguishing mutations in the African SARS-CoV-2 genomes compared to genomes from other countries, including disease hotspots, we conducted in silico analyses and comparisons. Complete African SARS-CoV-2 genomes deposited in GISAID and NCBI databases as of June 2020 were downloaded and aligned with genomes from Wuhan, China and other SARS-CoV-2 hotspots. Using phylogenetic analysis and amino acid sequence alignments of the spike and replicase (NSP12) proteins, we searched for possible targets for vaccine coverage or potential therapeutic agents. Our results showed a similarity between the African SARS-CoV-2 genomes and genomes in countries including China, Brazil, France, the United Kingdom, Italy, France and the United States of America. This study shows for the first time, an in-depth analysis of the SARS-CoV-2 landscape across Africa and will potentially provide insights into specific mutations to relevant proteins in the SARS-CoV-2 genomes in African populations.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.05.284141v1?rss=1

Authors: Shakya, B. R., Teppo, H.-R., Rieppo, L.

Abstract:

Among skin cancers, melanoma is the lethal form and the leading cause of death in humans. Melanoma begins in melanocytes and is curable at early stages. Thus, early detection and evaluation of its metastatic potential are crucial for effective clinical intervention. Fourier transform infrared (FTIR) spectroscopy has gained considerable attention due to its versatility in detecting biochemical and biological features present in the samples. Changes in these features are used to differentiate between samples at different stages of the disease. Previously, FTIR spectroscopy has been mostly used to distinguish between healthy and diseased conditions. With this study, we aim to discriminate between different melanoma cell lines based on their FTIR spectra. Formalin-fixed paraffin embedded samples from three melanoma cell lines (IPC-298, SK-MEL-30 and COLO-800) were used. Statistically significant differences were observed in the prominent spectral bands of three cell lines along with shifts in peak positions. Partial least square discriminant analysis (PLS-DA) models built for the classification of three cell lines showed accuracies of 96.38 %, 95.96 % and 99.7 %, for the differentiation of IPC-298, SK-MEL-30 and COLO-800, respectively. The results suggest that FTIR spectroscopy can be used to differentiate between genetically different melanoma cells and thus potentially characterize the metastatic potential of melanoma.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.07.285767v1?rss=1

Authors: JO, F., AS, A., RD, I., Anumudu, C. I.

Abstract:

Schistosomiasis remains a public health problem in developing countries. An ideal diagnostic test capable of detecting parasites as early as possible after the onset of infection is therefore highly desired. The identification of biomarker proteins associated with active infection and immune response may constitute the basis for the development of a successful diagnostic test. The aim of this study is to contribute to the development of protein biomarkers for schistosomiasis using a bioinformatics approach. The homologues of 36 previously identified urine biomarker proteins from a Schistosoma mansoni database, were identified in other Schistosoma species using SMARTBLAST and analyzed for similarities and differences using multiple sequence alignment. Of the 36 S. mansoni biomarker proteins, 29 had homologues with both S. haematobium and S. japonicum or either of S. haematobium and S . japonicum . Most of the 29 S. mansoni biomarker proteins aligned with their homologues with many conserved regions. However, some vital biomarker proteins like venom allergen-like proteins, which had been proposed as a putative drug and vaccine target, showed more semi conserved regions in which the amino acids had similar shape but weakly similar properties. The predictions of 12 markers found in all three species also show that treatment of infections may possibly benefit from the investigational drug Artenimol and specific nutraceuticals or supplements. Experimental evaluation is needed to confirm the potential of the proteins as biomarkers for early diagnosis of schistosomiasis and associated bladder cancer.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.06.284828v1?rss=1

Authors: Quince, C., Nurk, S., Raguideau, S., James, R. S., Soyer, O. S., Summers, J. K., Limasset, A., Eren, A. M., Chikhi, R., Darling, A. E.

Abstract:

We introduce a novel bioinformatics pipeline, STrain Resolution ON assembly Graphs (STRONG), which identifies strains de novo, when multiple metagenome samples from the same community are available. STRONG performs coassembly, followed by binning into metagenome assembled genomes (MAGs), but uniquely it stores the coassembly graph prior to simplification of variants. This enables the subgraphs for individual single-copy core genes (SCGs) in each MAG to be extracted. It can then thread back reads from the samples to compute per sample coverages for the unitigs in these graphs. These graphs and their unitig coverages are then used in a Bayesian algorithm, BayesPaths, that determines the number of strains present, their sequences or haplotypes on the SCGs and their abundances in each of the samples. Our approach both avoids the ambiguities of read mapping and allows more of the information on co-occurrence of variants in reads to be utilised than if variants were treated independently, whilst at the same time exploiting the correlation of variants across samples that occurs when they are linked in the same strain. We compare STRONG to the current state of the art on synthetic communities and demonstrate that we can recover more strains, more accurately, and with a realistic estimate of uncertainty deriving from the variational Bayesian algorithm employed for the strain resolution. On a real anaerobic digestor time series we obtained strain-resolved SCGs for over 300 MAGs that for abundant community members match those observed from long Nanopore reads.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.03.282319v1?rss=1

Authors: Li, H., Wang, Z., Chai, S., Bai, X., Ding, G., Li, J., Xiao, Q., Miao, B., Lin, W., Feng, J., Gao, C., Li, Y., Li, B., Hu, W., Lin, J., Fu, Z., Xie, J., Li, Y.

Abstract:

Microtus fortis (M. fortis) so far is the only mammal host that exhibits intrinsic resistance against Schistosoma japonicum infection. However, the underlying molecular mechanisms of this intrinsic resistance are not yet known. Here we performed the first de novo genome assembly of M. fortis, comprehensive gene annotation and evolution analysis. Furthermore, we compared the recovery rate of schistosome, pathological change and liver transcriptome between non-permissive host M. fortis and susceptible host mouse at different time points after Schistosome infection. We reveal that Immune response of M. fortis and mouse is different in time and type. M. fortis activates immune and inflammatory responses on the 10th days post infection, involving in multiple pathways, such as leukocyte extravasation, antibody activation (especially IgG3), Fc-gamma receptor mediated phagocytosis, and interferon signaling cascade. The strong immune responses of M. fortis in early stages of infection play important roles in preventing the development of schistosome. On the contrary, intense immune response occurred in mouse in late stages of infection (28~42 days post infection), and cannot eliminate schistosome. Infected mouse suffers severe pathological injury and continuous decrease of important functions such as cell cycle and lipid metabolism. Our findings offer new insights to the intrinsic resistance mechanism of M. fortis against schistosome infection. The genome sequence also provides bases for future studies of other important traits in M. fortis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.04.283689v1?rss=1

Authors: Marceca, G. P., Distefano, R., Tomasello, L., Lagana, A., Russo, F., Calore, F., Romano, G., Bagnoli, M., Gasparini, P., Ferro, A., Acunzo, M., Ma, Q., Croce, C. M., Nigita, G.

Abstract:

MicroRNAs (miRNAs) are a class of regulatory small non-coding RNAs that modulate gene expression. As such, miRNAs are indirectly involved in most cellular mechanisms, including cell differentiation, proliferation, survival, and homeostasis. Several different mechanisms finely regulate miRNA expression levels and functions. Among these, miRNA editing is a type of epitranscriptomic modification that alters the original nucleotide sequence of selected miRNAs, possibly influencing their conformational state and target-binding ability. To date, adenosine-to-inosine (A-to-I) and cytosine-to-uracil (C-to-U) RNA editing are recognized as the "canonical" editing types, with the A-to-I type being the predominant one. A comprehensive resource explicitly dedicated to miRNA editing data collection is still missing. Here we present MiREDiBase, a manually curated catalog of editing events in miRNAs. Overall, the current version includes 3059 unique A-to-I and C-to-U editing sites occurring in 626 human miRNA transcripts and three different species of non-human primates. Each editing event is provided with essential information and key relevant details, including biological sample, detection method, enzyme-dependent affinity, editing level, and biological function. Editing events in human mature miRNAs were provided with miRNA-target predictions and enrichment analysis. Minimum free energy structures were inferred for editing events falling into pre-miRNA regions, aiming to help users to interpret the biological information. MiREDiBase represents a valuable tool for cell biology and biomedical research, continuously updated, improved, and expanded with new data from the literature. MiREDiBase is available at https://ncrnaome.osumc.edu/miredibase.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.04.283135v1?rss=1

Authors: Oksuz, A. C., Ayday, E., Gudukbay, U.

Abstract:

Genome data is a subject of study for both biology and computer science since the start of Human Genome Project in 1990. Since then, genome sequencing for medical and social purposes becomes more and more available and affordable. Genome data can be shared on public websites or with service providers. However, this sharing compromises the privacy of donors even under partial sharing conditions. We mainly focus on the liability aspect ensued by unauthorized sharing of these genome data. One of the techniques to address the liability issues in data sharing is watermarking mechanism. To detect malicious correspondents and service providers (SPs) -whose aim is to share genome data without individuals' consent and undetected-, we propose a novel watermarking method on sequential genome data using belief propagation algorithm. In our method, we have two criteria to satisfy. (i) Embedding robust watermarks so that the malicious adversaries can not temper the watermark by modification and are identified with high probability (ii) Achieving {varepsilon}-local differential privacy in all data sharings with SPs. For the preservation of system robustness against single SP and collusion attacks, we consider publicly available genomic information like Minor Allele Frequency, Linkage Disequilibrium, Phenotype Information and Familial Information. Our proposed scheme achieves 100% detection rate against the single SP attacks with only 3% watermark length. For the worst case scenario of collusion attacks (50% of SPs are malicious), 80% detection is achieved with 5% watermark length and 90% detection is achieved with 10% watermark length. For all cases, {varepsilon}'s impact on precision remained negligible and high privacy is ensured.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.04.282814v1?rss=1

Authors: Littmann, M., Heinzinger, M., Dallago, C., Olenyi, T., Rost, B.

Abstract:

Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation transfer by identifying sequence-similar proteins with known function or through prediction methods using evolutionary information. Here, we proposed predicting GO terms through annotation transfer based on proximity of proteins in the SeqVec embedding rather than in sequence space. These embeddings originated from deep learned language models (LMs) for protein sequences (SeqVec) transferring the knowledge gained from predicting the next amino acid in 250 million protein sequences. Replicating the conditions of CAFA3, our method reached an Fmax of 37{+/-} 2%, 50{+/-} 3%, and 57{+/-} 2% for BPO, MFO, and CCO, respectively. This was numerically close to the top ten methods that had participated in CAFA3. Restricting the annotation transfer to proteins with <20% pairwise sequence identity to the query, performance dropped (Fmax BPO 33{+/-} 2%, MFO 43{+/-} 3%, CCO 53{+/-} 2%); this still outperformed naive sequence-based transfer. Preliminary results from CAFA4 appeared to confirm these findings. Overall, this new method may help, in particular, to annotate novel proteins from smaller families or proteins with intrinsically disordered regions.

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